Triple
T36207787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nagaden |
E1047446
|
entity |
| Predicate | hasFormerRollingStockFrom |
P29565
|
FINISHED |
| Object | Tokyu Corporation |
—
|
NE NERFINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Tokyu Corporation | Statement: [Nagaden, hasFormerRollingStockFrom, Tokyu Corporation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFormerRollingStockFrom Context triple: [Nagaden, hasFormerRollingStockFrom, Tokyu Corporation]
-
A.
formerRollingStock
chosen
Indicates that an entity was previously used as rolling stock (e.g., railway vehicles) but no longer serves in that capacity.
-
B.
reusedRollingStockFrom
Indicates that rolling stock used in one context, service, or system was previously used in another specified context, service, or system.
-
C.
previousRollingStockOrigin
Indicates that one entity is the place or source from which the immediately preceding piece of rolling stock (e.g., train car or unit) originated.
-
D.
laterRollingStock
Indicates that one piece of rolling stock comes into use or service at a later time than another piece of rolling stock.
-
E.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e4214748190a76c986d2a1838c2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69fe7eb4b8348190bb19d35766189ed4 |
completed | May 9, 2026, 12:24 a.m. |
| PD | Predicate disambiguation | batch_69fe7c35d2148190ab952e54feda1e76 |
completed | May 9, 2026, 12:13 a.m. |
Created at: May 3, 2026, 4:09 p.m.